In the fabric of European commerce, the flow of coins tells a story of connectivity, economy, and people. This analysis, spanning from 2002 to 2011, uncovers the patterns of euro coin circulation within France, revealing the dynamics of European integration at a granular level.
Introduction
The introduction of the euro in 2002 was not just an economic revolution—it was a symbol of European unity. This report explores the amalgamation of euro coins in France, comparing the mixing with Belgium and Germany, and revealing the tempo of monetary intermingling.
The Mixing Melody of Euros in France
The first visualization, a multi-colored line graph, illustrates the total number of coins minted each year in France, Spain, and Germany. The surge in 2002 reflects the novelty of the euro, with a sharp spike in Spanish coins. However, as the novelty waned, so did the flux, stabilizing into a harmonic consistency by 2011.
Question 1
How are France residents’ coins (from all 12 EU countries) been mixed annually from 2002-2011? And what was the speed of mixing over the years?
Code
# Selecting desired variables for analysisdesired_variables <-FRANCE_2002_2011 %>%select(SURVEY, QFRA_1c:QSLVE_2e)# Change the data from wide to long, change the minted coins names, aggreate and count all annuallyaggregated_q1_data <-desired_variables %>%pivot_longer(cols = QFRA_1c:QSLVE_2e, names_to ="Coin_minted_in", values_to ="Count") %>%mutate(Coin_minted_in =str_replace(Coin_minted_in, "_.*", ""),SURVEY =as.Date(SURVEY, format ="%d/%m/%Y"),Year =year(SURVEY)) %>%group_by(Year, Coin_minted_in) %>%summarise(num_coins =sum(Count)) %>%drop_na()# calculate the percent contributions for each coin minted country.contrib_rate_data <-aggregated_q1_data %>%summarise(Total =sum(num_coins), Coin_minted_in,num_coins) %>%mutate(Total_wide = Total) %>%pivot_wider(names_from = Coin_minted_in, values_from = num_coins) %>%pivot_longer(cols =c(Total, QALL:QSLVE), names_to ="Coin_minted_in", values_to ="num_coins") %>%mutate(Contrib_rate =round((num_coins/Total_wide)*100,2)) %>%select(-Total_wide)
Code
# Data manipulation for tabs 1 and 2 of the plotsplot_data <-contrib_rate_data %>%group_by(Year) %>%top_n(4, num_coins) %>%mutate(Coin_minted_in =ifelse(Coin_minted_in =="QALL", "GER", Coin_minted_in),Coin_minted_in =ifelse(Coin_minted_in =="QESP", "ESP", Coin_minted_in),Coin_minted_in =ifelse(Coin_minted_in =="QFRA", "FRA", Coin_minted_in)) %>%ungroup() # Important for plotly, otherwise it wont add lines
Code
plot1 <-plot_ly(data = plot_data, x =~Year, y =~num_coins, type ='scatter', mode ='lines+markers',color =~Coin_minted_in, hoverinfo ='text',text =~paste("Year:", Year, "<br>Coin minted in:", Coin_minted_in, "<br>Coins:", num_coins)) %>%layout(title ="Number of Coins Minted Each Year by Coin Type",xaxis =list(title ="Year"),yaxis =list(title ="Number of Coins"))plot1
A Palette of Proportions
A stacked bar chart paints the evolving landscape of coin contributions by country over the years. Dominated by the deep blue of French-minted coins, the visual subtly integrates the warm tones of Spanish and German currencies. This proportional representation speaks to the predominance of domestic coins in circulation within France but also to the steady presence of neighbors, hinting at the cross-border movements of people and economies.
Code
data <-plot_data %>%filter(Coin_minted_in !="Total") %>%select(-num_coins) %>%ungroup()data <- data %>%group_by(Year) %>%arrange(Year, desc(Contrib_rate), .by_group =TRUE) %>%mutate(Coin_minted_in =factor(Coin_minted_in, levels =unique(Coin_minted_in))) %>%ungroup()# Ensure FRA is always at the bottomdata$Coin_minted_in <-factor(data$Coin_minted_in, levels =rev(levels(data$Coin_minted_in)))# Plot the data with FRA at the bottom of each stackplot2 <-plot_ly(data, x =~Year, y =~Contrib_rate, type ='bar', name =~Coin_minted_in, color =~Coin_minted_in, hoverinfo ='text',text =~paste("Year:", Year, "<br>Coin minted in:", Coin_minted_in, "<br>Contrib rate:", Contrib_rate, "%")) %>%layout(title ="Contribution Rate of Coins Minted by Country and Year",xaxis =list(title ="Year"),yaxis =list(title ="Contribution Rate (%)"),barmode ='stack')plot2
Velocity of Variety
Speed is of essence in the third chart, showcasing the rate at which these coins mixed within the French economy. With a dip in 2006, perhaps indicating a temporary slowdown in cross-border interactions, the overall trend indicates an acceleration. By 2011, the diversity of euros in French wallets reached a rhythm faster than ever before, symbolizing a Europe growing closer in its everyday transactions.
Code
# Get all the contrib rates for France as it contributes most dominantly.q3_plot_data <- plot_data %>%filter(Coin_minted_in =="FRA") %>%select(Year, Contrib_rate) %>%mutate(Speed =round(lag(Contrib_rate)-Contrib_rate,2))plot3 <-plot_ly(data = q3_plot_data, x =~Year, y =~Speed, type ='scatter', mode ='lines+markers',hoverinfo ='text',text =~paste("Year:", Year, "<br>Mixing Speed:", Speed, "%")) %>%layout(title ="Mixing Speed Rate of Coins Minted Each Year in France",xaxis =list(title ="Year"),yaxis =list(title ="Mixing Speed"))plot3
Comparing France Coinage with Belgium and Germany
Turning to our neighbors, slopegraphs compare the proportions of French, Spanish, and German coins in Belgian and German wallets. In Belgium, the diversity is richer, with a closer balance between domestic and foreign coins. Germany, however, shows a staunch loyalty to its mint, with an overwhelming majority of local coins still prevailing in 2005. These visuals not only reflect monetary movements but cultural connections and the invisible threads of European interdependence.
Question 2
How does France residents’ coins (from all 12 EU countries) compare to Belgium residents’ coins (from all 12 EU countries) in December 2003? Where the proportions similar?
library(CGPfunctions)slopegraph1 <-newggslopegraph(dataframe = FRA_BEL, Times = Survey_country, Measurement = Contrib_rate, Grouping = Coin_minted_in,Title ="France Residents’ Coins vs Belgium Residents’ Coins Proportions",SubTitle ="Survey data from 2003 showing top 3 Coin Minted EU contries for each survey country",Caption ="We can see that the survey countries each had the highest contribution rates for their own minted coins; among the two countries, \nBelgium seems to be mixing slghtly more minted coins and has more diversity and varied contributions compared to Germany in 2003 \nwhich has more than 87% of it's coins minted by itself. On the other hand, Germany appears in bothsurvey countries, but German minted coins are slghtly more prevelant in Belgium.")slopegraph1 +theme(plot.caption =element_text(hjust =0))
Question 3
How does France residents’ coins (from all 12 EU countries) compare to Germany residents’ coins (from all 12 EU countries) in December 2005? Where the proportions similar?
Code
# Selecting desired variables for analysisdesired_variables <-DB_GERMANY_2005 %>%select(QFRA_1c:QBEL_2e) %>%mutate(Year =2005)# Change the data from wide to long, change the minted coins names, aggreate and count all annuallyaggregated_q1_data <-desired_variables %>%pivot_longer(cols = QFRA_1c:QBEL_5c, names_to ="Coin_minted_in", values_to ="Count") %>%mutate(Coin_minted_in =str_replace(Coin_minted_in, "_.*", "")) %>%group_by(Year, Coin_minted_in) %>%summarise(num_coins =sum(Count)) %>%drop_na()# calculate the percent contributions for each coin minted country.contrib_rate_data_2 <-aggregated_q1_data %>%summarise(Total =sum(num_coins), Coin_minted_in,num_coins) %>%mutate(Total_wide = Total) %>%pivot_wider(names_from = Coin_minted_in, values_from = num_coins) %>%pivot_longer(cols =c(Total, QALL:QFRA), names_to ="Coin_minted_in", values_to ="num_coins") %>%mutate(Contrib_rate =round((num_coins/Total_wide)*100,2)) %>%select(-Total_wide)contrib_rate_data_GER <- contrib_rate_data_2 %>%filter(Coin_minted_in!="Total") %>%top_n(3, num_coins) %>%mutate(Coin_minted_in =ifelse(Coin_minted_in =="QALL", "GER", Coin_minted_in),Coin_minted_in =ifelse(Coin_minted_in =="QAUT", "AUT", Coin_minted_in),Coin_minted_in =ifelse(Coin_minted_in =="QFRA", "FRA", Coin_minted_in)) %>%mutate(Survey_country ="Germany 2005")contrib_rate_data_FRA <- plot_data %>%filter(Year ==2005, Coin_minted_in !="Total") %>%mutate(Survey_country ="France 2005")FRA_GER<-full_join(contrib_rate_data_FRA, contrib_rate_data_GER)
Code
slopegraph2<-newggslopegraph(dataframe = FRA_GER, Times = Survey_country, Measurement = Contrib_rate, Grouping = Coin_minted_in,Title ="France Residents’ Coins vs Germany Residents’ Coins Proportions",SubTitle ="Survey data from 2005 showing top 3 Coin Minted EU contries for each survey country",Caption ="We can see that the survey countries each had the highest contribution rates for their own minted coins; \namong the two countries, France seems to be mixing more minted coins and has more diversity and varied\ncontributions compared to Germany in 2005 which has more than 94% of it's coins minted by itself.")slopegraph2 +theme(plot.caption =element_text(hjust =0))
Conclusion
Through the lens of currency, we see the unfolding narrative of European integration. French wallets became more cosmopolitan over time, although more slowly than one might expect. The Belgian scene was a mosaic of movement, while German wallets remained more homogeneous.
